Machine Learning Imputation, Clustering and Survival Analysis for Longitudinal Proteomic Data R package MIML version 0.1.0 Imputes missing biomarker measurements in a wide longitudinal serum panel with gradient-boosted decision trees, groups the completed panel by Bayesian consensus clustering, and compares the resulting patient subgroups by Kaplan-Meier, log-rank and Cox analysis. The imputation learner is described in Ke et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree> and the clustering method in Lock and Dunson (2013) <doi:10.1093/bioinformatics/btt425>. Imputed values are conditional-mean predictions, so the procedure is a machine-learning single imputation; the completions carry no between-imputation variance and must not be pooled by Rubin's rules. Two panels from Gene Expression Omnibus accession 'GSE65622' are included, one for each survival endpoint. Software Depends: R (>= 4.1.0) Imports: lightgbm (>= 3.3.0), data.table, survival, stats, utils Suggests: BCClong, testthat (>= 3.0.0) Neelesh Kumar <neelesh2302@gmail.com> Comprehensive R Archive Network (CRAN) Neelesh Kumar [aut, cre], Atanu Bhattacharjee [aut], Gajendra K. Vishwakarma [aut], Tanmoy Majumdar [aut] GPL-3 2026-09-30 application/tgz https://CRAN.R-project.org/package=MIML doi:10.32614/CRAN.package.MIML